ttjD/:dZddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZdd lmZmZmZmZdd lmZmZmZmZdd lm Z ddl!m"cm#Z$ddl%m&Z&erddl'm(Z(m)Z)ddlm*Z*ddl+m,Z,dZ-d)dZ.dZ/d*dZ0d+dZ1 d,d-d%Z2d.d(Z3dS)/zH Table Schema builders https://specs.frictionlessdata.io/table-schema/ ) annotations) TYPE_CHECKINGAnycastN)option_context)lib) ujson_loads) timezones)find_stack_level) _registry) is_bool_dtypeis_integer_dtypeis_numeric_dtypeis_string_dtype)CategoricalDtypeDatetimeTZDtypeExtensionDtype PeriodDtype) DataFrame) to_offset)DtypeObjJSONSerializable)Series) MultiIndexz1.4.0xrreturnstrc"t|rdSt|rdSt|rdStj|dst |t tfrdStj|drdSt|rdSd S) a Convert a NumPy / pandas type to its corresponding json_table. Parameters ---------- x : np.dtype or ExtensionDtype Returns ------- str the Table Schema data types Notes ----- This table shows the relationship between NumPy / pandas dtypes, and Table Schema dtypes. ============== ================= Pandas type Table Schema type ============== ================= int64 integer float64 number bool boolean datetime64[ns] datetime timedelta64[ns] duration object str categorical any =============== ================= integerbooleannumberMdatetimemdurationstringany) rr rr is_np_dtype isinstancerrr)rs Z/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/pandas/io/json/_table_schema.pyas_json_table_typer+7s< y q   y !   x C Jq?K2P$Q$Qz C z   xucHtj|jjr|jj}t |dkr3|jjdkr#t jdtnNt |dkr;td|Dr"t jdt|S| d}|jj dkr)tj |jj|j_n|jjpd|j_|S) z?Sets index names to 'index' for regular, or 'level_x' for Multiindexz-Index name of 'index' is not round-trippable.) stacklevelc3@K|]}|dVdSlevel_N startswith.0rs r* z$set_default_names..ns.!F!FQ!,,x"8"8!F!F!F!F!F!Fr,ztypeenum constraintsorderedfreqUTCtzextDtype)dtyper>r+r)r categoriesrMlistrrNfreqstrrr is_utcrP get_timezonerr)arrrRr>fieldcatsrMzones r*!convert_pandas_type_to_json_fieldr\}s$ IE xx"5))**E %)**'- &T 3m"i E; ' ' ' *f E? + +'  EH % % #E$KK)%(33D$$$ #"d E> * *'!Jj Lr,str | CategoricalDtypec|d}|dkr|ddS|dkr|ddS|dkr|ddS|d kr|dd S|d krd S|d krg|dr d|ddS|dr/t|d}t|j}d|dSdS|dkrKd|vr'd|vr#t |dd|dSd|vrt j|dSdStd|)a  Converts a JSON field descriptor into its corresponding NumPy / pandas type Parameters ---------- field A JSON field descriptor Returns ------- dtype Raises ------ ValueError If the type of the provided field is unknown or currently unsupported Examples -------- >>> convert_json_field_to_pandas_type({"name": "an_int", "type": "integer"}) 'int64' >>> convert_json_field_to_pandas_type( ... { ... "name": "a_categorical", ... "type": "any", ... "constraints": {"enum": ["a", "b", "c"]}, ... "ordered": True, ... } ... ) CategoricalDtype(categories=['a', 'b', 'c'], ordered=True, categories_dtype=str) >>> convert_json_field_to_pandas_type({"name": "a_datetime", "type": "datetime"}) 'datetime64[ns]' >>> convert_json_field_to_pandas_type( ... {"name": "a_datetime_with_tz", "type": "datetime", "tz": "US/Central"} ... ) 'datetime64[ns, US/Central]' rJr&rQNrint64r!float64r boolr% timedelta64r#rPzdatetime64[ns, ]rNzperiod[zdatetime64[ns]r'rLrMrK)rSrMobjectz#Unsupported or invalid field type: )getrr_freqstrrregistryfind ValueError)rYtypoffsetrNs r*!convert_json_field_to_pandas_typerlsR -C hyyT***  yyW--- yyY///  yyV,,,  }   99T?? $3U4[333 3 YYv   $uV}--Fv&&/D$T$$$ $##  E ! !i5&8&8# /7yAQ 5 =z!233 38 @3@@ A AAr,TrDDataFrame | Seriesr/ra primary_key bool | Noneversionc |durt|}i}g}|r|jjdkrptd|j|_t |jj|jjdD].\}}t|}||d<||/n'|t|j|j dkr=| D]'\} } |t| (n"|t|||d<|r?|jj r3|1|jjdkr|jj g|d<n|jj|d<n|||d<|r t|d <|S) a Create a Table schema from ``data``. This method is a utility to generate a JSON-serializable schema representation of a pandas Series or DataFrame, compatible with the Table Schema specification. It enables structured data to be shared and validated in various applications, ensuring consistency and interoperability. Parameters ---------- data : Series or DataFrame The input data for which the table schema is to be created. index : bool, default True Whether to include ``data.index`` in the schema. primary_key : bool or None, default True Column names to designate as the primary key. The default `None` will set `'primaryKey'` to the index level or levels if the index is unique. version : bool, default True Whether to include a field `pandas_version` with the version of pandas that last revised the table schema. This version can be different from the installed pandas version. Returns ------- dict A dictionary representing the Table schema. See Also -------- DataFrame.to_json : Convert the object to a JSON string. read_json : Convert a JSON string to pandas object. Notes ----- See `Table Schema `__ for conversion types. Timedeltas as converted to ISO8601 duration format with 9 decimal places after the seconds field for nanosecond precision. Categoricals are converted to the `any` dtype, and use the `enum` field constraint to list the allowed values. The `ordered` attribute is included in an `ordered` field. Examples -------- >>> from pandas.io.json._table_schema import build_table_schema >>> df = pd.DataFrame( ... {'A': [1, 2, 3], ... 'B': ['a', 'b', 'c'], ... 'C': pd.date_range('2016-01-01', freq='D', periods=3), ... }, index=pd.Index(range(3), name='idx')) >>> build_table_schema(df) {'fields': [{'name': 'idx', 'type': 'integer'}, {'name': 'A', 'type': 'integer'}, {'name': 'B', 'type': 'string', 'extDtype': 'str'}, {'name': 'C', 'type': 'datetime'}], 'primaryKey': ['idx'], 'pandas_version': '1.4.0'} Tr.r)strictr>fieldsN primaryKeypandas_version)rFr/rBrziplevelsr<r\appendndimitems is_uniquer>TABLE_SCHEMA_VERSION) rDr/rnrpschemarslevelr> new_fieldcolumnss r*build_table_schemarsJ }} &&F F I :  ! !lDJ77DJ"4:#4dj6FtTTT ) ) t=eDD $( &! i(((( ) MM;DJGG H H H y1}} @ @IFA MM;A>> ? ? ? ? @  7==>>>F8 +%++*= :  " "$(JO#4F< #':#3F<  *|8#7 Mr, precise_floatrct||}d|ddD}t|d||}d|ddD}d|vrtd t d d 5||}d d d n #1swxYwYd |dvr{||dd }t|jj dkr|jj dkr d |j_ n d|jj D|j_ |S)a  Builds a DataFrame from a given schema Parameters ---------- json : A JSON table schema precise_float : bool Flag controlling precision when decoding string to double values, as dictated by ``read_json`` Returns ------- df : DataFrame Raises ------ NotImplementedError If the JSON table schema contains either timezone or timedelta data Notes ----- Because :func:`DataFrame.to_json` uses the string 'index' to denote a name-less :class:`Index`, this function sets the name of the returned :class:`DataFrame` to ``None`` when said string is encountered with a normal :class:`Index`. For a :class:`MultiIndex`, the same limitation applies to any strings beginning with 'level_'. Therefore, an :class:`Index` name of 'index' and :class:`MultiIndex` names starting with 'level_' are not supported. See Also -------- build_table_schema : Inverse function. pandas.read_json )rcg|] }|d Sr>r7rYs r* z&parse_table_schema..wsFFF5vFFFr,r}rsrD)columnsc:i|]}|dt|Sr)rlrs r* z&parse_table_schema..zs7  f 8??r,rbz.s::; X..5Ar,) r rrINotImplementedErrorrastype set_indexr=r/r<r>)jsonrtable col_orderdfdtypess r*parse_table_schemarRsH M : : :EFFE(OH,EFFFI 5=) 4 4 4Y ?B8_X.F  ''! J    7 ? ? YYv  uX&& \\%/,7 8 8 rx~  ! # #x}'' $ ?Ax~BHN IsB66B:=B:)rrrr)rrG)rr])TNT) rDrmr/rarnrorprarrG)rrarr)4__doc__ __future__rtypingrrrr?pandas._configr pandas._libsrpandas._libs.jsonr pandas._libs.tslibsr pandas.util._exceptionsr pandas.core.dtypes.baser rgpandas.core.dtypes.commonr rrrpandas.core.dtypes.dtypesrrrrpandasrpandas.core.commoncorecommonr:pandas.tseries.frequenciesrpandas._typingrrrpandas.core.indexes.multirr|r+rFr\rlrrrr,r*rs #""""" ))))))))))))))))))444444999999  0000005 444444++++\0@IBIBIBIB\# fffffR@@@@@@r,